{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to read data in batches from TFRecords with TensorFlow Eager\n",
    "----\n",
    "\n",
    "Hello everyone, this tutorial is again focused on the input pipeline. It is quite simple, but I remember when I first started reading data in batches I got stuck in quite a few details so I thought that I might share my methods here. I really hope it will be useful for some of you.\n",
    "\n",
    "### Tutorials flowchart\n",
    "----\n",
    "![img](tutorials_graphics/readbatches.png)\n",
    "\n",
    "We are going to work on two cases:\n",
    "* **input data of variable sequence length** - in this case we will pad the batch on the fly to the biggest sequence length.\n",
    "* **image data**\n",
    "\n",
    "\n",
    "The data for both cases has been stored as TFRecords. You can have a look at the [**4th**](https://github.com/madalinabuzau/tensorflow-eager-tutorials/blob/master/04_text_data_to_tfrecords.ipynb) and [**5th**](https://github.com/madalinabuzau/tensorflow-eager-tutorials/blob/master/05_images_to_tfrecords.ipynb) tutorial to see how I transfer raw data to TFRecords.\n",
    "\n",
    "So, let's jump right into coding :)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import here useful libraries\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library for data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Make the plots appear inline in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Import TensorFlow and TensorFlow Eager\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib.eager as tfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable eager mode. Once activated it cannot be reversed! Run just once.\n",
    "tfe.enable_eager_execution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: Reading data of variable sequence length\n",
    "----\n",
    "The first part of this tutorial shows you how to read input data that comes in different lengths. In our case, we used dummy IMDB reviews from the Large Movie Database. As you can imagine, each review has a different number of words. Therefore, when we will be reading a batch of data we will pad the sequences to the maximum sequence length within a batch.\n",
    "\n",
    "To see how I obtained sequences of word indexes, along with the label and the sequence length please see [this tutorial](https://github.com/madalinabuzau/tensorflow-eager-tutorials/blob/master/04_text_data_to_tfrecords.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Create function to parse each TFRecord\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_imdb_sequence(record):\n",
    "    '''\n",
    "    Script to parse imdb tfrecords.\n",
    "    \n",
    "    Returns:\n",
    "        token_indexes: sequence of token indexes present in the review.\n",
    "        target: the target of the movie review.\n",
    "        sequence_length: the length of the sequence.\n",
    "    '''\n",
    "    context_features = {\n",
    "        'sequence_length': tf.FixedLenFeature([], dtype=tf.int64),\n",
    "        'target': tf.FixedLenFeature([], dtype=tf.int64),\n",
    "        }\n",
    "    sequence_features = {\n",
    "        'token_indexes': tf.FixedLenSequenceFeature([], dtype=tf.int64),\n",
    "        }\n",
    "    context_parsed, sequence_parsed = tf.parse_single_sequence_example(record, \n",
    "        context_features=context_features, sequence_features=sequence_features)\n",
    "        \n",
    "    return (sequence_parsed['token_indexes'], context_parsed['target'],\n",
    "            context_parsed['sequence_length'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Create dataset iterator\n",
    "----\n",
    "\n",
    "As you can see in the function above, after parsing each record, we return a sequence of word indexes, the target of the review and the sequence length. In the method *padded_batch* we only pad the first element of the record: the sequence of word indexes. The target and sequence length do not need to be padded as they are just a single number, in each example. Thus, the padded_shapes will be:\n",
    "* [None] -> pad the sequences to the largest dimension, unknown yet, therefore None.\n",
    "* [ ] -> no padding for the target.\n",
    "* [ ] -> no padding for the sequence length.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select here the batch size\n",
    "batch_size = 2\n",
    "\n",
    "# Create dataset from TFRecords\n",
    "dataset = tf.data.TFRecordDataset('datasets/dummy_text/dummy.tfrecords')\n",
    "dataset = dataset.map(parse_imdb_sequence).shuffle(buffer_size=10000)\n",
    "dataset = dataset.padded_batch(batch_size, padded_shapes=([None],[],[]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Iterate through data once \n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 1], shape=(2,), dtype=int64)\n",
      "tf.Tensor([1 0], shape=(2,), dtype=int64)\n",
      "tf.Tensor([0 1], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for review, target, sequence_length in tfe.Iterator(dataset):\n",
    "    print(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 145)\n",
      "(2, 139)\n",
      "(2, 171)\n"
     ]
    }
   ],
   "source": [
    "for review, target, sequence_length in tfe.Iterator(dataset):\n",
    "    print(review.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([137 151], shape=(2,), dtype=int64)\n",
      "tf.Tensor([139 171], shape=(2,), dtype=int64)\n",
      "tf.Tensor([145 124], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for review, target, sequence_length in tfe.Iterator(dataset):\n",
    "    print(sequence_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2: Read images (and their labels) in batches\n",
    "----\n",
    "\n",
    "In the second part of the tutorial, we are going to visualize the images stored as TFRecords, by reading them in batches. These images are a small subsample from the FER2013 dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Create function to parse each record and decode image\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parser(record):\n",
    "    '''\n",
    "    Function to parse a TFRecords example. \n",
    "    \n",
    "    Returns:\n",
    "        img: decoded image.\n",
    "        label: the corresponding label of the image.         \n",
    "    '''\n",
    "    \n",
    "    # Define here the features you would like to parse\n",
    "    features = {'image': tf.FixedLenFeature((), tf.string),\n",
    "                'label': tf.FixedLenFeature((), tf.int64)}\n",
    "    \n",
    "    # Parse example\n",
    "    parsed = tf.parse_single_example(record, features)\n",
    "\n",
    "    # Decode image \n",
    "    img = tf.image.decode_image(parsed['image'])\n",
    "   \n",
    "    return img, parsed['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Create dataset iterator\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select here the batch size\n",
    "batch_size = 5\n",
    "\n",
    "# Create dataset from TFRecords\n",
    "dataset = tf.data.TFRecordDataset('datasets/dummy_images/dummy.tfrecords')\n",
    "dataset = dataset.map(parser).shuffle(buffer_size=10000)\n",
    "dataset = dataset.batch(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Iterate through dataset once. Visualize images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff81c113b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff81c113a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dictionary that stores the correspondence between integer labels and the emotions\n",
    "emotion_cat = {0:'Angry', 1:'Disgust', 2:'Fear', 3:'Happy', 4:'Sad', 5:'Surprise', 6:'Neutral'}\n",
    "\n",
    "# Go through the dataset once\n",
    "for image, label in tfe.Iterator(dataset):\n",
    "    # Create a subplot for each batch of images\n",
    "    f, axarr = plt.subplots(1, int(image.shape[0]), figsize=(14, 6))\n",
    "    # Plot images\n",
    "    for i in range(image.shape[0]):\n",
    "        axarr[i].imshow(image[i,:,:,0], cmap='gray')\n",
    "        axarr[i].set_title('Emotion: %s' %emotion_cat[label[i].numpy()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please let me know if you would like me to add anything to this tutorial. I will do my best to add it :)!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
